Question

我正在尝试使用scipy中的kmeans聚类，正是这里的聚类：

http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans.html#scipy.cluster.vq.kmeans

我要做的是转换列表列表，如下所示：

data without_x[
[0, 0, 0, 0, 0, 0, 0, 20.0, 1.0, 48.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1224.0, 125.5, 3156.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 22.5, 56.0, 41.5, 85.5, 0, 0, 0, 0, 0, 0, 0, 0, 1495.0, 3496.5, 2715.0, 5566.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
]

进入一个ndarry以便与Kmeans方法一起使用它。当我尝试将列表列表转换为ndarray时，我得到一个空数组，从而排除了整个分析。 ndarray的长度是可变的，它取决于收集的样本数量。但是我可以轻松搞定 LEN（data_without_x）

以下是返回空列表的代码片段。

import numpy as np
import "other functions"

data, data_without_x = data_preparation.generate_sampled_pdf()
nodes_stats, k, list_of_list= result_som.get_number_k()

data_array = np.array(data_without_x)
whitened = whiten(data_array)
centroids, distortion = kmeans(whitened, int(k), iter=100000)

这就是我得到的输出只是保存在一个简单的日志文件中：

___________________________
this is the data array[[ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 ..., 
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]]
___________________________
This is the whitened array[[ nan  nan  nan ...,  nan  nan  nan]
 [ nan  nan  nan ...,  nan  nan  nan]
 [ nan  nan  nan ...,  nan  nan  nan]
 ..., 
 [ nan  nan  nan ...,  nan  nan  nan]
 [ nan  nan  nan ...,  nan  nan  nan]
 [ nan  nan  nan ...,  nan  nan  nan]]
___________________________

当我尝试将列表列表转换为numpy.array时，有没有人知道会发生什么？

感谢您的帮助

Answer 1

这正是如何将列表列表转换为python中的ndarray。您确定您的data_without_x填写正确吗？在我的机器上：

data = [[1,2,3,4],[4,5,6,7,8]]
data_arr = np.array(data)

data_arr
array([[1,2,3,4],
       [5,6,7,8]])

我认为你期待的行为

查看您的输入内容有很多零...请记住，打印输出并不能显示所有内容。您可能只是看到所有＆＃34;零＆＃34;从你的输入。检查特定的非零元素以确保

Answer 2

vq.whiten和vq.kmeans期待一个形状(M, N)的数组，其中每一行是一个观察点。转换你的data_array：

import numpy as np
import scipy.cluster.vq as vq
np.random.seed(2013)    

data_without_x = [
    [0, 0, 0, 0, 0, 0, 0, 20.0, 1.0, 48.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        1224.0, 125.5, 3156.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 22.5, 56.0, 41.5, 85.5, 0, 0, 0, 0, 0, 0, 0, 0, 1495.0,
        3496.5, 2715.0, 5566.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
]


data_array = np.array(data_without_x).T
whitened = vq.whiten(data_array)

centroids, distortion = vq.kmeans(whitened, 5)
print(centroids)

产量

[[  1.22649791e+00   2.69573144e+00]
 [  3.91943108e-03   5.57406434e-03]
 [  5.73668382e+00   4.83161524e+00]
 [  0.00000000e+00   1.29763133e+00]]

Answer 3

使用numpy的asarray功能。这很简单：参考：https://docs.scipy.org/doc/numpy/reference/generated/numpy.asarray.html

列表到ndarray

3 个答案: